similar to: Problems with predict.lm: incorrect SE estimate (PR#7772)

Full_Name: Marek Ancukiewicz Version: 1.8.1 OS: Linux Submission from: (NULL) (132.183.12.87) Function cor() incorrectly handles missing observation with method="spearman": > x <- c(1,2,3,NA,5,6) > y <- c(4,NA,2,5,1,3) > cor(x,y,use="complete.obs",method="s") [1] -0.1428571 >

Full_Name: Marek Ancukiewicz Version: 2.10.1 OS: Linux Submission from: (NULL) (74.0.49.2) Both cor() and cor.test() incorrectly handle ordered variables with method="kendall", cor() incorrectly handles ordered variables for method="spearman" (method="person" always works correctly, while method="spearman" works for cor.test, but not for cor()). In

Full_Name: Marek Ancukiewicz Version: 0.64.0 OS: Linux (RedHat 6.0) Submission from: (NULL) (132.183.12.87) I've noticed that factor structures get recoded when the data is dumped using either dump or dput and then restored with source or dget. This occurs when the values taken by factors do not include 1. For example: a <-

Hello everyone, I am searching for a covariate selection procedure in a cox model formulated as a counting process. I use intervals, my formula looks like coxph(Surv(start,stop,status)~ x1+x2+...+cluster(id),robust=T) where id is a country code (I study occurence of civil wars from 1962 to 1997). I'd like something not based on p-values, since they have several flaws for this purpose. I turned

if I want to specify y-coordinates for the heights of horizontal lines to go across a plot. x <- c(1,2,3,6,4,8,4,7) y <- c(3,2,7,4,5,4,5,6) h <- c(3,5,7) plot(x,y) abline(y=h) However I got error message: Warning message: parameter "y" couldn't be set in high-level plot() function (I tried abline(h=y) , it;s not what I want also) Do u know why?

Dear All It is a little hard to give a good small example of my question,so I will show the full data on the bottom and the attachment.Maybe some one could tell me an appropriate way to show it.I'm sorry for the inconvenience. Q:How to generate a 53*53 diagonal matrix by my data Some problems confused me are that: 1.Since it is a diagonal matrix,I have tried to transform col1 and col2 to

Hello people, I am in the process of migrating from Stata to R and I would like to check if my results are similar under the two softwares: Here is my GLM command under R nurse.model<-glm(pQSfteHT~dQSvacrateHTQuali3_2 + dQSvacrateHTQuali3_3 + dQSvacrateHTQuali3_4 + dQSvacrateHTQuali3_5 + cluster_32 + cluster_33 + cluster_34 ,family=binomial(link = "logit")) and below the stata

Hello, I've encountered a very weird issue with the method subset(), or maybe this is something I don't know about said method that when you're subsetting based on the columns of a data frame you can only use constants (0.1, 2.3, 2.2) instead of variables? Here's a look at my data frame called 'ea.cad.pwr': *>ea.ca.pwr[1:5,] MAF OR POWER 1 0.02 0.01 0.9999 2 0.02

Hello, R-helpers. I have a data frame below called df. I want to add a variable c, based on the following logic: if df$a < 5 then the first two times this condition is met place a 1 in c, the next two times this condition is met place a 2 in c and when the condition is not met let c equal df$b. The result would look like, dfnew, shown below. a <- rep(c(3,5,7), 4) b <- rep(c(NA,1,2),

Hi folks, For unknown cause I encounter following mounting problem; # /mnt/hda8 mount: wrong fs type, bad option, bad superblock on /dev/hda8, missing codepage or other error In some cases useful info is found in syslog - try dmesg | tail or so # dmesg | tail via82cxxx: timeout while reading AC97 codec (0x9A0000) via82cxxx: timeout while reading AC97 codec (0x9A0000)

Hi There, While looking through the mailing list archive, I did not come across a simple minded example regarding the creation of dummy variables. The Gauss language provides the command "y = dummydn(x,v,p)" for creating dummy variables. Here: x = Nx1 vector of data to be broken up into dummy variables. v = Kx1 vector specifying the K-1 breakpoints p = positive integer in the range

Folks, 1. Is there a more appropriate list (r-devel?) for posting such suggestions? I am a newbie to R, and doubtless will have some suggestions for the documentation -- some good, others not quite so. I would actually like to help give back to the community (I was motivated by Prof. Ripley's 2001 talk in which he had commented that open source software users rarely give back anything.) --

Folks, I asked a question on this mailing list about the subset support of lm(). In a flash, I got three helpful responses from Rajarshi Guha <rxg218 at psu.edu> <http://jijo.cjb.net> Erin Hodgess <hodgess at gator.uhd.edu> and Peter Dalgaard <p.dalgaard at biostat.ku.dk> :-) and it was just great. The mistake I was making was in not understanding the notion of a

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

Hi, I'm trying to calculate the passwords so i can change them from a php-gui, an as i see it, userPassword can have any form if the cryptation algorithm is stated before the hash, like "{md5}blahblah32bit". In the docs, it states that sambaNTPassword is a md4 hash, wich should be calculatable (haven't done it yet though). And last for the sambaLMPassword: I can't find

Hi, I have a experiment with block, plots, sub-plots, and sub-sub-plots with repeated measures and 3 factors (factorial design) when we have been observed diameter (mm), high (cm) and leaves number (count). However, we don't have one treatment in one factor, so, my design is unbalanced. On a previous message here, a friend tell me that "It appears to me that your design is a split-split

Full_Name: Russell Lenth Version: 2.6.2 OS: Windows XP Pro Submission from: (NULL) (128.255.132.36) The poly() function allows a higher-degree polynomial than it should, when raw=FALSE. For example, consider 5 distinct 'x' values, each repeated twice. we can fit a polynomial of degree 8: ===== R> x = rep(1:5, 2) R> y = rnorm(10) R> lm(y ~ poly(x, 8)) Call: lm(formula = y ~

This is a summary and extension of the thread "GLMM (lme4) vs. glmmPQL output" http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html In the new revision (#Version: 0.4-7) of lme4 the standard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the revision, and to G?ran Brostr?m who run a simulation. In response to my first posting, Prof.

Hello R-users, I have been trying to fit what I think is a simple mixed-effects model using lmer (from lme4), but I've run into some difficulty that I have not been able to resolve using the existing archives or Pinheiro and Bates (2000). I am measuring populations (of birds) which change with time at a number of different sites. These sites are grouped into regions. Sites are not measured

Greetings, In my opinion the documentation or behaviour of qr.solve, qr.coef, qr.resid, and qr.fitted is not easily comprehensible and unfortunate. We all know that a linear system Ax=b can have 0, one or infinitely many solutions. To treat all these cases uniformly we can rephrase the problem as x = argmin_u||Au-b||,